HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification
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Abstract (arXiv)
Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each sentence independently, ignoring inter-sentence contextual signals that human annotators naturally exploit. We present HierBias, a hierarchical context-conditioned media bias detector that formally models document context in bias prediction. We introduce the context-conditioned bias probability and prove theoretically that leveraging document context strictly reduces the Bayes error of sentence-level classification when inter-sentence mutual information is non-zero. A multi-task generalization bound further establishes that jointly training binary bias detection and fine-grained bias type classification improves sample efficiency on small annotated corpora. Architecturally, HierBias pairs a sentence-level RoBERTa encoder with a cross-sentence Transformer aggregator and dual output heads for binary detection and four-class type classification. Evaluated on BABE and BASIL, HierBias achieves 0.853 F1 and 0.723 MCC, surpassing the state-of-the-art bias-detector by +2.6% F1 and +4.3% MCC (McNemar's test, p < 0.05). Ablation experiments confirm that each theoretical component contributes independently and consistently.
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References · 24
- 2017Attention Is All You Needcited by 60,106
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
- 2024Visual In-Context Learning for Large Vision-Language Modelscited by 124
- 2024Measuring Political Bias in Large Language Models: What Is Said and How It Is Saidcited by 89
- 2023Thread of Thought Unraveling Chaotic Contextscited by 71
- 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
- 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 55
- 2022ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classificationcited by 45
- 2025Improving Medical Large Vision-Language Models with Abnormal-Aware Feedbackcited by 42
- 2024Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Modelscited by 41
- 2021EventBERT: A Pre-Trained Model for Event Correlation Reasoningcited by 40
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- 2024MemoryMamba: Memory-Augmented State Space Model for Defect Recognitioncited by 24
- 2024Residual-based Language Models are Free Boosters for Biomedical Imagingcited by 23
- 2023Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collectioncited by 17
- 2022A Domain-adaptive Pre-training Approach for Language Bias Detection in Newscited by 16
- 2024The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detectioncited by 15
- 2023The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Biascited by 12
- 2026Condition Errors Refinement in Autoregressive Image Generation with Diffusion Losscited by 9
- 2025Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coveragecited by 6
- 2025To Bias or Not to Bias: Detecting bias in News with bias-detectorcited by 5
- 2024Experiments in News Bias Detection with Pre-Trained Neural Transformerscited by 2
- 2024Improved Models for Media Bias Detection and Subcategorizationcited by 1
- 2025Bridging Human and Model Perspectives: A Comparative Analysis of Political Bias Detection in News Media Using Large Language Modelscited by 1
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